Nonparametric estimation of varying-coefficient single-index models

作者：Kim Young Ju^*

来源：Journal of Applied Statistics, 2015, 42(2): 281-291.

DOI：10.1080/02664763.2014.947358

• smoothing splines
• single-index
• varying-coefficient functions
• Bayesian confidence interval
• penalized likelihood

The varying-coefficient single-index model has two distinguishing features: partially linear varying-coefficient functions and a single-index structure. This paper proposes a nonparametric method based on smoothing splines for estimating varying-coefficient functions and an unknown link function. Moreover, the average derivative estimation method is applied to obtain the single-index parameter estimates. For interval inference, Bayesian confidence intervals were obtained based on Bayes models for varying-coefficient functions and the link function. The performance of the proposed method is examined both through simulations and by applying it to Boston housing data.

• 出版日期2015-2-1